Create E3C.py
Browse files
E3C.py
ADDED
@@ -0,0 +1,302 @@
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1 |
+
# pip install bs4 syntok
|
2 |
+
|
3 |
+
import os
|
4 |
+
import random
|
5 |
+
|
6 |
+
import datasets
|
7 |
+
|
8 |
+
import numpy as np
|
9 |
+
from bs4 import BeautifulSoup, ResultSet
|
10 |
+
from syntok.tokenizer import Tokenizer
|
11 |
+
|
12 |
+
tokenizer = Tokenizer()
|
13 |
+
|
14 |
+
_CITATION = """\
|
15 |
+
@report{Magnini2021, \
|
16 |
+
author = {Bernardo Magnini and Begoña Altuna and Alberto Lavelli and Manuela Speranza \
|
17 |
+
and Roberto Zanoli and Fondazione Bruno Kessler}, \
|
18 |
+
keywords = {Clinical data,clinical enti-ties,corpus,multilingual,temporal information}, \
|
19 |
+
title = {The E3C Project: \
|
20 |
+
European Clinical Case Corpus El proyecto E3C: European Clinical Case Corpus}, \
|
21 |
+
url = {https://uts.nlm.nih.gov/uts/umls/home}, \
|
22 |
+
year = {2021}, \
|
23 |
+
}
|
24 |
+
"""
|
25 |
+
|
26 |
+
_DESCRIPTION = """\
|
27 |
+
E3C is a freely available multilingual corpus (English, French, Italian, Spanish, and Basque) \
|
28 |
+
of semantically annotated clinical narratives to allow for the linguistic analysis, benchmarking, \
|
29 |
+
and training of information extraction systems. It consists of two types of annotations: \
|
30 |
+
(i) clinical entities (e.g., pathologies), (ii) temporal information and factuality (e.g., events). \
|
31 |
+
Researchers can use the benchmark training and test splits of our corpus to develop and test \
|
32 |
+
their own models.
|
33 |
+
"""
|
34 |
+
|
35 |
+
_URL = "https://github.com/hltfbk/E3C-Corpus/archive/refs/tags/v2.0.0.zip"
|
36 |
+
|
37 |
+
_LANGUAGES = ["English","Spanish","Basque","French","Italian"]
|
38 |
+
|
39 |
+
class E3C(datasets.GeneratorBasedBuilder):
|
40 |
+
|
41 |
+
BUILDER_CONFIGS = [
|
42 |
+
datasets.BuilderConfig(name=f"{lang}_clinical", version="1.0.0", description=f"The {lang} subset of the E3C corpus") for lang in _LANGUAGES
|
43 |
+
]
|
44 |
+
|
45 |
+
BUILDER_CONFIGS += [
|
46 |
+
datasets.BuilderConfig(name=f"{lang}_temporal", version="1.0.0", description=f"The {lang} subset of the E3C corpus") for lang in _LANGUAGES
|
47 |
+
]
|
48 |
+
|
49 |
+
DEFAULT_CONFIG_NAME = "French_clinical"
|
50 |
+
|
51 |
+
def _info(self):
|
52 |
+
|
53 |
+
if self.config.name == "default":
|
54 |
+
self.config.name = self.DEFAULT_CONFIG_NAME
|
55 |
+
|
56 |
+
if self.config.name.find("clinical") != -1:
|
57 |
+
names = ["O","B-CLINENTITY","I-CLINENTITY"]
|
58 |
+
elif self.config.name.find("temporal") != -1:
|
59 |
+
names = ["O", "B-EVENT", "B-ACTOR", "B-BODYPART", "B-TIMEX3", "B-RML", "I-EVENT", "I-ACTOR", "I-BODYPART", "I-TIMEX3", "I-RML"]
|
60 |
+
|
61 |
+
features = datasets.Features(
|
62 |
+
{
|
63 |
+
"id": datasets.Value("string"),
|
64 |
+
"text": datasets.Value("string"),
|
65 |
+
"tokens": datasets.Sequence(datasets.Value("string")),
|
66 |
+
"ner_tags": datasets.Sequence(
|
67 |
+
datasets.features.ClassLabel(
|
68 |
+
names=names,
|
69 |
+
),
|
70 |
+
),
|
71 |
+
}
|
72 |
+
)
|
73 |
+
|
74 |
+
return datasets.DatasetInfo(
|
75 |
+
description=_DESCRIPTION,
|
76 |
+
features=features,
|
77 |
+
citation=_CITATION,
|
78 |
+
supervised_keys=None,
|
79 |
+
)
|
80 |
+
|
81 |
+
def _split_generators(self, dl_manager):
|
82 |
+
|
83 |
+
data_dir = dl_manager.download_and_extract(_URL)
|
84 |
+
|
85 |
+
print(data_dir)
|
86 |
+
|
87 |
+
if self.config.name.find("clinical") != -1:
|
88 |
+
|
89 |
+
print("clinical")
|
90 |
+
|
91 |
+
return [
|
92 |
+
datasets.SplitGenerator(
|
93 |
+
name=datasets.Split.TRAIN,
|
94 |
+
gen_kwargs={
|
95 |
+
"filepath": os.path.join(data_dir, "E3C-Corpus-2.0.0/data_annotation", self.config.name.replace("_clinical",""), "layer2"),
|
96 |
+
"split": "train",
|
97 |
+
},
|
98 |
+
),
|
99 |
+
datasets.SplitGenerator(
|
100 |
+
name=datasets.Split.VALIDATION,
|
101 |
+
gen_kwargs={
|
102 |
+
"filepath": os.path.join(data_dir, "E3C-Corpus-2.0.0/data_annotation", self.config.name.replace("_clinical",""), "layer2"),
|
103 |
+
"split": "validation",
|
104 |
+
},
|
105 |
+
),
|
106 |
+
datasets.SplitGenerator(
|
107 |
+
name=datasets.Split.TEST,
|
108 |
+
gen_kwargs={
|
109 |
+
"filepath": os.path.join(data_dir, "E3C-Corpus-2.0.0/data_annotation", self.config.name.replace("_clinical",""), "layer1"),
|
110 |
+
"split": "test",
|
111 |
+
},
|
112 |
+
),
|
113 |
+
]
|
114 |
+
|
115 |
+
elif self.config.name.find("temporal") != -1:
|
116 |
+
|
117 |
+
print("temporal")
|
118 |
+
|
119 |
+
return [
|
120 |
+
datasets.SplitGenerator(
|
121 |
+
name=datasets.Split.TRAIN,
|
122 |
+
gen_kwargs={
|
123 |
+
"filepath": os.path.join(data_dir, "E3C-Corpus-2.0.0/data_annotation", self.config.name.replace("_temporal",""), "layer1"),
|
124 |
+
"split": "train",
|
125 |
+
},
|
126 |
+
),
|
127 |
+
datasets.SplitGenerator(
|
128 |
+
name=datasets.Split.VALIDATION,
|
129 |
+
gen_kwargs={
|
130 |
+
"filepath": os.path.join(data_dir, "E3C-Corpus-2.0.0/data_annotation", self.config.name.replace("_temporal",""), "layer1"),
|
131 |
+
"split": "validation",
|
132 |
+
},
|
133 |
+
),
|
134 |
+
datasets.SplitGenerator(
|
135 |
+
name=datasets.Split.TEST,
|
136 |
+
gen_kwargs={
|
137 |
+
"filepath": os.path.join(data_dir, "E3C-Corpus-2.0.0/data_annotation", self.config.name.replace("_temporal",""), "layer1"),
|
138 |
+
"split": "test",
|
139 |
+
},
|
140 |
+
),
|
141 |
+
]
|
142 |
+
|
143 |
+
@staticmethod
|
144 |
+
def get_annotations(entities: ResultSet, text: str) -> list:
|
145 |
+
|
146 |
+
return [[
|
147 |
+
int(entity.get("begin")),
|
148 |
+
int(entity.get("end")),
|
149 |
+
text[int(entity.get("begin")) : int(entity.get("end"))],
|
150 |
+
] for entity in entities]
|
151 |
+
|
152 |
+
def get_clinical_annotations(self, entities: ResultSet, text: str) -> list:
|
153 |
+
|
154 |
+
return [[
|
155 |
+
int(entity.get("begin")),
|
156 |
+
int(entity.get("end")),
|
157 |
+
text[int(entity.get("begin")) : int(entity.get("end"))],
|
158 |
+
entity.get("entityID"),
|
159 |
+
] for entity in entities]
|
160 |
+
|
161 |
+
def get_parsed_data(self, filepath: str):
|
162 |
+
|
163 |
+
for root, _, files in os.walk(filepath):
|
164 |
+
|
165 |
+
for file in files:
|
166 |
+
|
167 |
+
with open(f"{root}/{file}") as soup_file:
|
168 |
+
|
169 |
+
soup = BeautifulSoup(soup_file, "xml")
|
170 |
+
text = soup.find("cas:Sofa").get("sofaString")
|
171 |
+
|
172 |
+
yield {
|
173 |
+
"CLINENTITY": self.get_clinical_annotations(soup.find_all("custom:CLINENTITY"), text),
|
174 |
+
"EVENT": self.get_annotations(soup.find_all("custom:EVENT"), text),
|
175 |
+
"ACTOR": self.get_annotations(soup.find_all("custom:ACTOR"), text),
|
176 |
+
"BODYPART": self.get_annotations(soup.find_all("custom:BODYPART"), text),
|
177 |
+
"TIMEX3": self.get_annotations(soup.find_all("custom:TIMEX3"), text),
|
178 |
+
"RML": self.get_annotations(soup.find_all("custom:RML"), text),
|
179 |
+
"SENTENCE": self.get_annotations(soup.find_all("type4:Sentence"), text),
|
180 |
+
"TOKENS": self.get_annotations(soup.find_all("type4:Token"), text),
|
181 |
+
}
|
182 |
+
|
183 |
+
def _generate_examples(self, filepath, split):
|
184 |
+
|
185 |
+
all_res = []
|
186 |
+
|
187 |
+
key = 0
|
188 |
+
|
189 |
+
parsed_content = self.get_parsed_data(filepath)
|
190 |
+
|
191 |
+
for content in parsed_content:
|
192 |
+
|
193 |
+
for sentence in content["SENTENCE"]:
|
194 |
+
|
195 |
+
tokens = [(
|
196 |
+
token.offset + sentence[0],
|
197 |
+
token.offset + sentence[0] + len(token.value),
|
198 |
+
token.value,
|
199 |
+
) for token in list(tokenizer.tokenize(sentence[-1]))]
|
200 |
+
|
201 |
+
filtered_tokens = list(
|
202 |
+
filter(
|
203 |
+
lambda token: token[0] >= sentence[0] and token[1] <= sentence[1],
|
204 |
+
tokens,
|
205 |
+
)
|
206 |
+
)
|
207 |
+
|
208 |
+
tokens_offsets = [
|
209 |
+
[token[0] - sentence[0], token[1] - sentence[0]] for token in filtered_tokens
|
210 |
+
]
|
211 |
+
|
212 |
+
clinical_labels = ["O"] * len(filtered_tokens)
|
213 |
+
clinical_cuid = ["CUI_LESS"] * len(filtered_tokens)
|
214 |
+
temporal_information_labels = ["O"] * len(filtered_tokens)
|
215 |
+
|
216 |
+
for entity_type in ["CLINENTITY","EVENT","ACTOR","BODYPART","TIMEX3","RML"]:
|
217 |
+
|
218 |
+
if len(content[entity_type]) != 0:
|
219 |
+
|
220 |
+
for entities in list(content[entity_type]):
|
221 |
+
|
222 |
+
annotated_tokens = [
|
223 |
+
idx_token
|
224 |
+
for idx_token, token in enumerate(filtered_tokens)
|
225 |
+
if token[0] >= entities[0] and token[1] <= entities[1]
|
226 |
+
]
|
227 |
+
|
228 |
+
for idx_token in annotated_tokens:
|
229 |
+
|
230 |
+
if entity_type == "CLINENTITY":
|
231 |
+
if idx_token == annotated_tokens[0]:
|
232 |
+
clinical_labels[idx_token] = f"B-{entity_type}"
|
233 |
+
else:
|
234 |
+
clinical_labels[idx_token] = f"I-{entity_type}"
|
235 |
+
clinical_cuid[idx_token] = entities[-1]
|
236 |
+
else:
|
237 |
+
if idx_token == annotated_tokens[0]:
|
238 |
+
temporal_information_labels[idx_token] = f"B-{entity_type}"
|
239 |
+
else:
|
240 |
+
temporal_information_labels[idx_token] = f"I-{entity_type}"
|
241 |
+
|
242 |
+
if self.config.name.find("clinical") != -1:
|
243 |
+
_labels = clinical_labels
|
244 |
+
elif self.config.name.find("temporal") != -1:
|
245 |
+
_labels = temporal_information_labels
|
246 |
+
|
247 |
+
all_res.append({
|
248 |
+
"id": key,
|
249 |
+
"text": sentence[-1],
|
250 |
+
"tokens": list(map(lambda token: token[2], filtered_tokens)),
|
251 |
+
"ner_tags": _labels,
|
252 |
+
})
|
253 |
+
|
254 |
+
key += 1
|
255 |
+
|
256 |
+
if self.config.name.find("clinical") != -1:
|
257 |
+
|
258 |
+
if split != "test":
|
259 |
+
|
260 |
+
ids = [r["id"] for r in all_res]
|
261 |
+
|
262 |
+
random.seed(4)
|
263 |
+
random.shuffle(ids)
|
264 |
+
random.shuffle(ids)
|
265 |
+
random.shuffle(ids)
|
266 |
+
|
267 |
+
train, validation = np.split(ids, [int(len(ids)*0.8738)])
|
268 |
+
|
269 |
+
if split == "train":
|
270 |
+
allowed_ids = list(train)
|
271 |
+
elif split == "validation":
|
272 |
+
allowed_ids = list(validation)
|
273 |
+
|
274 |
+
for r in all_res:
|
275 |
+
if r["id"] in allowed_ids:
|
276 |
+
yield r["id"], r
|
277 |
+
else:
|
278 |
+
|
279 |
+
for r in all_res:
|
280 |
+
yield r["id"], r
|
281 |
+
|
282 |
+
elif self.config.name.find("temporal") != -1:
|
283 |
+
|
284 |
+
ids = [r["id"] for r in all_res]
|
285 |
+
|
286 |
+
random.seed(4)
|
287 |
+
random.shuffle(ids)
|
288 |
+
random.shuffle(ids)
|
289 |
+
random.shuffle(ids)
|
290 |
+
|
291 |
+
train, validation, test = np.split(ids, [int(len(ids)*0.70), int(len(ids)*0.80)])
|
292 |
+
|
293 |
+
if split == "train":
|
294 |
+
allowed_ids = list(train)
|
295 |
+
elif split == "validation":
|
296 |
+
allowed_ids = list(validation)
|
297 |
+
elif split == "test":
|
298 |
+
allowed_ids = list(test)
|
299 |
+
|
300 |
+
for r in all_res:
|
301 |
+
if r["id"] in allowed_ids:
|
302 |
+
yield r["id"], r
|